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Record W2069582825 · doi:10.1029/2007gl032300

Carbon monoxide pollution from cities and urban areas observed by the Terra/MOPITT mission

2008· article· en· W2069582825 on OpenAlexfundno aff
Cathy Clerbaux, D. P. Edwards, M. N. Deeter, L. K. Emmons, Jean‐François Lamarque, Xue Xi Tie, S. T. Massie, J. C. Gille

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersCentro Internacional de Agricultura TropicalUniversity of TorontoNational Center for Atmospheric ResearchNational Science Foundation
KeywordsEnvironmental scienceTropospherePollutionDaytimeAtmospheric sciencesCarbon monoxideAir pollutionAtmosphere (unit)SatellitePlanetary boundary layerRemote sensingMeteorologyGeographyGeology

Abstract

fetched live from OpenAlex

Carbon monoxide (CO) is a key species for tracking pollution plumes. The Measurement Of Pollution in The Troposphere (MOPITT) mission onboard the Terra satellite has already provided 7.5 years of CO atmospheric concentration measurements around the globe. Limited sensitivity to the boundary layer is well known to be a weakness of nadir looking thermal infrared sounders. This paper investigates the possibility of using the MOPITT surface measurements to detect CO emitted by cities and urban centers. By selecting the data and averaging them over long time periods, we demonstrate that the CO pollution arising from the large cities and urban areas can be distinguished from the background transported pollution. The more favorable observations are obtained during daytime and at locations where the thermal contrast (temperature gradient) between the surface and lower atmosphere is significant.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.249
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations83
Published2008
Admission routes1
Has abstractyes

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